一个都不能少：面向序列推荐的在线自监督自蒸馏方法
Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation
AI总结：
针对序列推荐中数据稀疏下行为有限用户的自监督信号挖掘难题，提出$S^4$Rec范式，结合在线聚类、对抗学习和自蒸馏实现行为丰富用户向有限用户的知识迁移，经四数据集验证有效。
摘要：
序列推荐方法在现代推荐系统中发挥着核心作用。其面临的一项关键挑战是在数据稀疏的情况下准确建模用户偏好。为应对这一挑战，近期的方法利用对比学习（CL），通过最大化原始用户行为序列的两个增强视图的互信息来自监督信号。尽管这类方法具备有效性，但基于CL的方法在为行为数据有限的用户充分挖掘自监督信号方面存在局限，因为行为丰富的用户自然能提供更多信息。为解决该问题，我们提出一种名为Online Self-Supervised Self-distillation for Sequential Recommendation（$S^4$Rec，序列推荐在线自监督自蒸馏）的新型学习范式，有效打通了自监督学习与自蒸馏方法之间的壁垒。具体而言，我们采用在线聚类，根据用户不同的潜在意图对其进行高效分组。此外，我们利用对抗学习策略确保聚类过程不受行为长度因素的影响。随后，我们采用自蒸馏机制，推动知识从行为丰富的用户（教师）向行为有限的用户（学生）迁移。在四个真实世界数据集上开展的实验验证了所提方法的有效性。
英文摘要：
Sequential recommendation methods play a pivotal role in modern recommendation systems. A key challenge lies in accurately modeling user preferences in the face of data sparsity. To tackle this challenge, recent methods leverage contrastive learning (CL) to derive self-supervision signals by maximizing the mutual information of two augmented views of the original user behavior sequence. Despite their effectiveness, CL-based methods encounter a limitation in fully exploiting self-supervision signals for users with limited behavior data, as users with extensive behaviors naturally offer more information. To address this problem, we introduce a novel learning paradigm, named Online Self-Supervised Self-distillation for Sequential Recommendation ($S^4$Rec), effectively bridging the gap between self-supervised learning and self-distillation methods. Specifically, we employ online clustering to proficiently group users by their distinct latent intents. Additionally, an adversarial learning strategy is utilized to ensure that the clustering procedure is not affected by the behavior length factor. Subsequently, we employ self-distillation to facilitate the transfer of knowledge from users with extensive behaviors (teachers) to users with limited behaviors (students). Experiments conducted on four real-world datasets validate the effectiveness of the proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
